Large, global banks process billions of transactions across service offerings to a plethora of customers across demographics, daily. In order to sustain effective operations, they must adopt cutting-edge analytics that churn the petabytes of rich information into valuable insights.

As of today, most global banks are processing these petabytes of transactional data through legacy and modern databases that get downgraded through years of mergers and acquisitions. Therefore, migrating complete legacy and distributed data towards a robust storage solution that addresses cur-rent challenges and future requirements, marks the first step towards modernization.

Having said that, banks also have to make sense of two data formats -- unstructured and un-leveraged format from legacy databases, and structured data from new tools in big data and analytics. Towards that, they must implement solutions that center on R, Python, SAS, or NoSQL driven analytics. Not only do these solutions integrate structured and unstructured information, but also process it like a fast moving Pac-Man! In fact, they produce unbelievable outcomes, occasionally influencing strategic outcomes and are mostly open source. At the same time, Blockchain technology is a new kid on the block! To maximize value from the opportunities that Blockchain presents, banks require top-class analytical and data processing capabilities.

Therefore, business analytics is an invaluable capability for organizations. It augments competitiveness of service offerings, market growth, and relevance from the current perspective. Simultaneously, cog-nitive / predictive analytics, which has been neglected for quite some time, is equally important to en-sure anti-money laundering (AML) / fraud detection. For a considerable amount of time, banks have overlooked the hazards of incomplete and missing information across Know Your Customer (KYC), Know Your Employee (KYE), Customer IDentification (CID), Customer Due Diligence (CDD), and En-hanced Due Diligence (EDD) processes, while driving competitiveness. Today, such negligence can prove costly and I can extend the point in discussion to Cash Management services which Banks ex-tend as a value-add for a small fee. In the current scenario, cash management teams must focus atten-tion towards cognitive / predictive analytics for AML / fraud detection. This is because these teams directly handle cash coming from external sources, which could be honest or obscure with a dark un-derbelly. Consequently, services that cater to cash collection, dropbox, vault, sweeps, zero balance accounts, and cash concentration are abused. This is because some of these overlap with the realms of private banking, which hide the true beneficiary behind the wraps of secret arrangements and agree-ments.

In my next blog, I will discuss about how applying R, Python, or SAS on the available 'structured' infor-mation combined with available 'unstructured or free' information will come handy towards harness-ing the power of fast and efficient analytics, using underlying legacy and modern silos.